Alignment of mixed-modality data sets for reduction and removal of imaging artifacts

ABSTRACT

Methods and systems are described for removing reflective artifacts from an imaging model. An x-ray detector captures x-ray images that include a structure and an imaging device captures a surface scan of the same structure. An image processor constructs a three-dimensional CT model of the structure from the x-ray images and constructs a three-dimensional surface model of the structure from the surface scan. The image processor is configured to resize and orient the surface model and/or the CT model so that they are the same scale and orientation, overlay the surface model onto the CT model, and detect data points in the combined data set that extend beyond a surface of the structure in the surface model. The detected data points represent artifacts in the CT model and are adjusted by interpolation to produce an artifact-reduced CT model.

RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 15/595,826, filed May 15, 2017, and entitled “ALIGNMENT OFMIXED-MODALITY DATA SETS FOR REDUCTION AND REMOVAL OF IMAGINGARTIFACTS,” which is a continuation of U.S. patent application Ser. No.14/714,603, filed on May 18, 2015, and entitled “ALIGNMENT OFMIXED-MODALITY DATA SETS FOR REDUCTION AND REMOVAL OF IMAGINGARTIFACTS,” which is a continuation-in-part of U.S. patent applicationSer. No. 13/090,786, filed on Apr. 20, 2011 and entitled “REDUCTION ANDREMOVAL OF ARTIFACTS FROM A THREE-DIMENSIONAL DENTAL X-RAY DATA SETUSING SURFACE SCAN INFORMATION,” which claims priority to U.S.Provisional Patent Application No. 61/326,031 filed on Apr. 20, 2010,the entire contents of all of which are herein incorporated byreference.

BACKGROUND

The present invention relates to dental imaging technology. Morespecifically, the present invention relates to reconstructing athree-dimensional image of a patient's teeth using both x-ray andsurface scanning technology.

X-ray technology can be used to generate a three-dimensional, digitalrepresentation of a subject using computed tomography (CT). However,metal and other objects can reflect x-rays that would otherwisepenetrate through human tissue and be detected by the x-ray detector.This reflection can cause unwanted artifacts to appear in the captureddata set. This effect is particularly prevalent in dental imaging whereforeign substances such as metal fillings or braces are often installedin the patient's mouth.

There are some prior systems in which artifacts are removed from x-raydata by, simply stated, “combining” x-ray and non-x-ray data. However,as best known by the inventors, in addition to removing or reducingartifacts, such systems also remove significant amounts of desired imagedata.

U.S. Publication No. 2010/0124367 has suggested that artifacts can beremoved from x-ray data by the “fusion of the x-ray data set with anoptical image of the jaw, which is completely free of metal artifacts .. . .” However, details regarding how the artifacts would be removed arenot provided and the “fusion” disclosed in the '367 publication uses apre-positioning technique that requires identifying registration pointson a screen or other manual means prior to combining the data. Whilethis pre-positioning makes the task of combining the two data setssubstantially easier than a completely automatic method, the methodrequires manual intervention. That is, the x-ray technician, dentist, orother dental professional must manually manipulate the images on ascreen.

U.S. Pat. No. 6,671,529 describes a method of creating a composite skullmodel by combining three-dimensional CT data and laser surface scans ofa patient's teeth. In the '529 patent, the teeth are completely removedfrom the CT model and replaced with only the surface scan data of thepatient's teeth.

U.S. Pat. No. 7,574,025 describes a method of removing artifacts from athree-dimensional model (such as CT or MRI) by a negative impressiontemplate of the patient's teeth. In the '025 patent, a negativeimpression template is cast of the patient's teeth. A first model isgenerated while the negative impression template is placed in thepatient's mouth. A second model is generated of only the negativeimpression template using the same imaging technology as the first.Voxels from the first digital image are substituted for correspondingvoxels from the second digital image to create a model of the patient'steeth without artifacts.

SUMMARY

It would be useful to have an improved method and system of removingartifacts from x-ray data that did not remove significant portions ofdesired CT image data, substitute data from multiple x-rays, or requiremanual pre-positioning of the data sets.

In some embodiments, the invention provides a system for generating athree-dimensional, digital representation including a patient's teethusing both CT and surface scanning data. The system includes an x-raysource and an x-ray detector that are used to capture several x-rayimages. The images are transmitted to an image processing system wherethey are used to construct a three-dimensional CT model of the patient'steeth. The system also includes a surface scanner (such as a laser orstructured light scanning system) that captures data representing theshape and texture of the surface of the patient's teeth. The surfacedata is also transmitted to the image processing system where it is usedto construct a three-dimensional model of the surface of the patient'steeth. The image processing system then resizes and orients the surfacemodel and the CT model so that the two models are of the same scale andorientation.

In some embodiments, the surface model is then overlaid onto the CTmodel. This is achieved without requiring manual intervention. Thesystem of this embodiment then detects artifacts in the CT model bydetecting any data points in the CT model that extend beyond theoverlaid surface model. Data points extending beyond the surface modelare considered to be artifacts and the image processing system removesthe artifact data points from the CT model. In other embodiments, anydata points in the CT model that extend beyond the surface model areprocessed to determine whether they are artifacts. Processed data pointsthat are identified as artifacts are then removed from the CT model. Insome embodiments, after the artifact data points are identified andremoved from the CT model, the overlaid surface data is then removedleaving only the three-dimensional CT model.

In some embodiments, the surface model is forward projected to createprojection data in the same two-dimensional (2D) format as the CTprojection data. The forward projected data is combined with the CTprojection data to identify regions of metal and teeth and allow the CTreconstruction to remove the effects of metal from the reconstructed CTimages. Again, this is achieved without requiring manual pre-positioningof the two sets of data with respect to one another.

In another embodiment, the invention provides a method for removingreflective artifacts from an imaging model of a patient's teeth. A firstvolumetric model and a second volumetric model of the patient's teethare accessed from a computer-readable memory. The orientation and scaleof at least one of the two models is repeatedly and automaticallyadjusted until an optimized orientation and scale is determined thatcorrelates the first volumetric model and the second volumetric model.The second volumetric model is then overlaid onto the first volumetricmodel. Any data points in the first volumetric model that extend beyonda surface of the patient's teeth in the second volumetric model aredetected and removed to create an artifact-reduced volumetric model.

In yet another embodiment, the invention provides a system for removingreflective artifacts from an imaging model of a patient's teeth. Thesystem includes an x-ray source, an x-ray detector that captures x-rayimages, a surface scanner that captures a surface scan of the patient'steeth, and an imaging processing system. The image processing systemconstructs a three-dimensional CT model of the patient's teeth from thex-ray images and constructs a three-dimensional surface model of thepatient's teeth from the surface scan. The image processor is alsoconfigured to repeatedly and automatically adjust an orientation and ascale of at least one of the two volumetric models until an optimizedorientation and scale are determined that correlates the firstvolumetric model and the second volumetric model. The second volumetricmodel is then overlaid onto the first volumetric model. Any points inthe first volumetric model that extend beyond a surface of the patient'steeth in the second volumetric model are detected and removed to createan artifact-reduced volumetric model.

In still another embodiment, the invention provides a method ofautomatically aligning a first volumetric model of a patient's teeth anda second volumetric model of the same patient's teeth by repeating aseries of acts. The repeated acts include evaluating an alignment of thefirst volumetric model and the second volumetric model, adjusting avariable of the first volumetric model or the second volumetric model(the variable being randomly selected from a group consisting of a yaw,a pitch, a roll, and a scale), evaluating an alignment of the firstvolumetric model and the second volumetric model after adjusting thevariable, accepting the adjustment of the variable if the alignment ofthe first volumetric model and the second volumetric model is improvedafter the adjustment of the variable, generating a random thresholdnumber, accepting the adjustment of the variable if the alignment of thefirst volumetric model and the second volumetric model is not improvedafter the adjustment of the variable and a calculated acceptanceprobability exceeds the random threshold number, rejecting theadjustment of the variable if the alignment of the first volumetricmodel and the second volumetric model is not improved after theadjustment of the variable and the calculated acceptance probabilitydoes not exceed the threshold number, and adjusting a probabilityvariable used to calculate the acceptance probability, wherein adjustingthe probability variable reduces the likelihood that the calculatedacceptance probability will exceed the random threshold number on eachsubsequent repeat iteration.

Other aspects of the invention will become apparent by consideration ofthe detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram illustrating the components of the imagingsystem according to one embodiment of the invention.

FIG. 1B is a diagram of a cone-beam CT scanning system used in thesystem of FIG. 1A.

FIG. 2 is a flowchart showing the method of using the system of FIG. 1Ato remove artifacts from a CT model.

FIG. 3 is a flowchart showing a second method of using the system ofFIG. 1A to remove artifacts from a CT model.

FIG. 4 is a flowchart showing a third method of using the system of FIG.1A to remove artifacts from a CT model.

FIG. 5 is an image of a slice of the CT data captured by the x-raydetector of FIG. 1A.

FIG. 6 is a perspective view of an unfiltered CT model of a patient'steeth and jaw with imaging artifacts.

FIG. 7 is an overhead view of a surface model of a patient's teethcaptured by the surface scanner of FIG. 1A.

FIG. 8 is an image of a planar silhouette from the surface model of FIG.5 overlaid onto a slice of the CT data of FIG. 3.

FIG. 9 is a perspective view of the CT model of FIG. 4 with the imagingartifacts removed.

FIG. 10 is a flowchart of a method for determining an optimizedalignment of a surface model and a CT model.

DETAILED DESCRIPTION

Before any embodiments of the invention are explained in detail, it isto be understood that the invention is not limited in its application tothe details of construction and the arrangement of components set forthin the following description or illustrated in the following drawings.The invention is capable of other embodiments and of being practiced orof being carried out in various ways.

FIG. 1A is a block diagram illustrating the components of a system forremoving artifacts from a three-dimensional digital CT model of apatient's teeth. The system can also be used to create three-dimensionaldigital models of the patient's jaw and other facial bones and tissue.The system includes an x-ray source 101 and an x-ray detector 103. Thex-ray source 101 is positioned to project x-rays toward a patient'steeth. The x-ray detector 103 is positioned on the opposite side of thepatient's teeth—either inside the patient's oral cavity or on theopposite side of the patient's head. The x-rays from the x-ray source101 are attenuated differently by the patient's tissue and are detectedby the x-ray detector 103.

The x-ray detector 103 is connected to an image processing system 105.The data captured by the x-ray detector 103 is used by the imageprocessing system to generate a three-dimensional CT model of thepatient's teeth. As such, in one embodiment, the x-ray source 101 andthe x-ray detector 103 are part of a cone-beam, scanning CT system thatrotates around the patient's head to collect x-ray image data asillustrated in FIG. 1B. An example of one such scanning system isdescribed in U.S. application Ser. No. 12/700,028 filed on Feb. 4, 2010,the entire contents of which are incorporated herein by reference. The'028 application relates to motion correction, but the imagingcomponents—sensor and source mounted on a rotatable C-arm, areapplicable to the techniques described herein.

The system illustrated in FIG. 1A also includes a surface scanningimaging system 107. The surface scanning system 107 captures datarelating to the surface texture, size, and geometry of the patient'steeth. The captured surface data is then transmitted to the imageprocessing system 105 where it is used to generate a three-dimensionalsurface model of the patient's teeth. In some embodiments, the surfacescanning imaging system includes a laser transceiver system such as oneincluding a laser source 107A and a laser sensor or digital camera 107B.The laser source scans a laser line across the surface of the patient'stooth. The sensor or camera captures images of the projected line. Theimage processing system 105 analyzes how the shape of the laser linefrom the perspective of the sensor or camera changes as it is scannedacross the patient's tooth. This data is then used to generate thethree-dimensional surface model of the patient's teeth.

The image processing system 105 in FIG. 1A includes a processor 109 forexecuting computer instructions and a memory 111 for storing theinstructions and data transmitted from the x-ray detector 103 and thesurface scanning system 107. In some embodiments, the image processingsystem includes one or more desktop computers running image processingsoftware. In other embodiments, the image processing system 105 is adevice designed specifically for processing image data received from thex-ray detector 103 and the surface scanning system 107. Data captured bythe x-ray detector 103 and the surface scanning imaging system 107 aswell as processed volumetric data is displayed on a display 113.

FIG. 2 is a flowchart illustrating one method for how the system of FIG.1A can be used to generate to a CT model of a patient's teeth and removeartifacts from a CT model of the patient's teeth. The system begins bycapturing cone-beam CT (CBCT) image data of the patient's teeth (step201). This is done by rotating the x-ray source 101 and the x-raydetector 103 around the patient's head to collect several x-ray imagesof the patient's teeth. The captured data is then used to generate athree-dimensional CT model of the patient's teeth (step 203). Thesurface scanning system 107 is used to capture optical surface data(step 205), which the image processing system 105 then uses to generatea three-dimensional surface model of the patient's teeth (step 207). Invarious embodiments, the surface data can be captured before or afterthe CT image is captured. Similarly, the CT data can be processed by theimage processing system (step 203) before or after the surface data iscaptured by the surface scanning system 107 (step 205).

After both the CT model and the surface model have been generated, theimage processing system 105 correlates the three-dimensional CT volumemodel and the three-dimensional optical surface model to determine aproper scale and orientation of the two models. The surface model isoverlaid onto the CT model to generate a combined data set (step 209).In some embodiments, the system is calibrated such that the captureddata includes registration information indicating the location andperspective from which the data was captured. In such embodiments, theproper scale and orientation of the two models is determined by matchingthe registration information from the CT model to the correspondingregistration information from the surface model.

In other embodiments, the image processing system 105 uses surfacematching algorithms to identify corresponding physical structures inboth of the models. The identification of corresponding structures canbe achieved, for example, by the identification of three or moreanatomical landmarks that appear in both of the two models and thenrotating, translating, and scaling one model until the differencesbetween these landmarks is minimized within a predetermined tolerance.Alternatively, the entire surface in the two models can be matched byscaling, rotation, and translation through various well-knownoptimization techniques such as simulated annealing. A specificmechanism for determining an optimized orientation and scale of the twomodels based on the principle of simulated annealing is discussed infurther detail below. A number of features in the two models can becharacterized in each and correlated to determine the best match of thesurfaces. The image processing system 105 sizes and orients the modelsaccording to the matching structures.

In some embodiments, the overlay process can be executed by overlayingthe entire surface model onto the entire CT model. The image processingsystem 105 can also include various functions that segment the CT modelinto sub-volumes. The sub-volume functions can be used to isolate asingle tooth from the CT model. In FIGS. 5-8, artifacts caused by x-rayreflective materials (such as metal filings) are removed from the CTmodel by overlaying data from the surface model onto sub-volumes of theCT model one tooth at a time.

FIG. 5 shows a single horizontal slice from the CT model of thepatient's teeth. Artifacts 301 can be seen reflecting from structures inthe patient's teeth 303. FIG. 6 shows the fully constructed CT model.Again, artifacts 301 are clearly visible extending from the patient'steeth 303. These artifacts 301 hide some of the surface details of thepatient's teeth 303 in the CT model. FIG. 7 shows a portion of thesurface model generated by the image processing system 105 based on thesurface information captured by the surface scanning system 107. Theimage processing system isolates a horizontal cut plane 501 in thesurface model. This cut plane 501 corresponds to the slice from the CTmodel illustrated in FIG. 5. Because the surface model does not containany data points from within the patient's teeth, the cut plane dataidentifies a silhouette shape 503 that corresponds to the outer surfaceof the patient's tooth on a given horizontal plane. In FIG. 8, thesilhouette shape 503 from the cut plane 501 is overlaid onto the sametooth in the slice from the CT model.

After the silhouette data 503 from the surface model is overlaid ontothe corresponding tooth in a slice of the CT model, the image processingsystem 105 identifies data points in the CT model that extend beyond thesilhouette 503 (step 211). If data is detected outside of the silhouetteshape 503, the system determines whether this data is artifact data. Insome embodiments, all data in the CT model that extends beyond thesilhouette shape 503 is assumed to be or is identified as artifact dataand is removed from the CT model. In other embodiments, the data outsideof the silhouette 503 is processed by a filtering or interpolationalgorithm. The interpolation algorithm detects picture elements in thedata just outside of the silhouette shape 503 that have densities thatare above a threshold. The algorithm then interpolates data for theseidentified, artifact-associated pixels (or data points) with data fromadjacent pixels not associated with artifact. FIG. 8 illustrates an area601 just outside of the silhouette shape 503 that is to be analyzed bysuch a filtering algorithm.

After the CT data outside of the silhouette shape 503 has beeninterpolated, adjusted, or removed, the image processing system 105moves onto another tooth in the same slice of CT data. After thenecessary corrections have been made for each tooth, the imageprocessing system 105 moves to another slice of CT data. This processrepeats until all of the teeth in each of the CT data slices have beenanalyzed and the artifact data has been removed.

After the CT data has been analyzed and the artifacts have beenidentified and removed, the image processing system 105 removes theoverlaid surface model and any silhouette shapes from the CT data (step213) and generates an artifact-reduced CT model. As pictured in FIG. 9,the artifacts have been removed from the artifact-reduced CT model andthe surface of each tooth is visible.

An alternative approach for combining the CT and optically derivedsurface data as presented in FIG. 3. As in the method of FIG. 2 above,the CT and optical data are both captured (steps 221, 225) and are bothprocessed to produce 3D volumetric data (steps 223, 227). The datamodels are again correlated and overlaid (step 229). Once correlated,the two volumetric datasets in the combined data set are then bothforward-projected in order to create a set of two-dimensional projectionimages (step 231). Optically-derived data is then used to identify theteeth surface and filter or compensate for the artifact-causing elements(created by scatter and beam hardening associated with metal in theimage volume) in the images or frames that lead to artifacts in thereconstructed images (step 233). The filtering process removes artifactsbefore a three-dimensional model is reconstructed from thetwo-dimensional projection images (step 235).

A third approach to combining the CT and optically-derived surface datais presented in FIG. 4. In this case, the CT and optical data are bothcaptured as described above (steps 241, 243). However, instead ofgenerating two three-dimensional models, the optical surface data isforward projected to generate two-dimensional projection images (step245). The two dimensional images from the CT data and thetwo-dimensional projection images are correlated and overlaid (step247). Artifact-causing elements that lead to artifacts in thereconstructed images are filtered from the raw CT data using the anatomydefined by the two-dimensional optical frames (step 249). Athree-dimensional CT model is then constructed from the filtered andcorrected CT data frames.

As discussed above, in some implementations, the system is configured toautomatically determine a proper orientation and scale to correlate thesurface model and the CT model. FIG. 10 illustrates an example of amethod for automatically determining an optimized alignment of the twodifferent models. According to the technique of FIG. 10, the scale andorientation of one of the models (e.g., either the CT model or thesurface model) remains stationary throughout the optimization process.The yaw, pitch, roll, and scale of the other model is repeatedlyadjusted until an optimized fit is determined. In this example, the CTmodel will remain stationary while the yaw, pitch, roll, and scale ofthe surface model is repeatedly adjusted.

First, a set of random yaw, pitch, roll, and scale values are generatedfor the surface model (step 1001). The center of the surface model sizedand oriented according to the random values is aligned with the centerof the CT model and a “fit” score (F₀) is calculated (step 1003). The“fit” score quantifies the degree to which the surface model aligns withthe CT model. For example, it can be a score calculated based on thedistance between each point on the surface model and the nearest“surface” point on the CT model. The “fit” score does not need to becapable of indicating when perfect alignment has been achieved—instead,it only need be capable of indicating when one orientation/scalepresents a better “fit” than another.

After the current fit score (F₀) is calculated, one of the fourorientation/scale variables is selected at random and altered by a fixedamount (step 1005). For example, if “roll” is randomly selected, the“roll” value is altered while the pitch, yaw, and scale values remainthe same. A new “fit” score (F₁) is calculated based on the updatedorientation/scale using the same formula as was used to calculate the“current fit score” (F₀) (step 1007).

Next, an acceptance probability is calculated based on the current fitscore (F₀), the new fit score (F₁), and a T value (step 1009). Asdiscussed further below, the T value begins at a relatively high valueand is regularly decremented with each loop of the optimization routineillustrated in FIG. 10. In some implementations, the acceptanceprobability value is calculated according to the equation:

$a = e^{\frac{F_{1} - F_{0}}{T}}$

As such, if the new fit score (F₁) is better than the current fit score(F₀), the acceptance probability will be greater than one (1). However,if the new fit score is less than the current fit score, the acceptanceprobability will be less than one and will increasingly approach zero asthe current fit score worsens. Similarly, as discussed in further detailbelow, the acceptance probability also increasingly approaches zero asthe value of T decreases with each iteration.

After the acceptance probability (a) is calculated, a random number (R)between zero and one is generated (step 1011) and the acceptanceprobability (a) is compared to the random number (R) (step 1013). If theacceptance probability (a) exceeds the random number (R), then theoptimization routine accepts the new randomly altered orientation/scaleof the surface model (step 1015). Otherwise, the random alteration isrejected (step 1017) and the orientation/scale of the surface modelremains as it was before the random alteration (in step 1005).

After the orientation/scale alteration is evaluated, the value of T isdecreased (step 1019) and as long as T is not yet less than or equal tozero (step 1021), the optimization routine proceeds to execute anotherloop by calculating a current fit score (step 1003) and altering one ofthe four variables (i.e., yaw, pitch, roll, or scale) at random (step1005). However, if T is less than or equal to zero after the decrease(step 1021), then the optimization routine is complete and the currentorientation and scale of the surface model is deemed to be optimallyaligned with the CT model (step 1023).

The optimization routine of FIG. 10 is based on the concept of simulatedannealing. The routine of FIG. 10 randomly tests small alterations ofthe orientation/scale of the surface model to see if the “fit” can beimproved. If the small alteration would improve the fit, the routinewill always accept the fit (i.e., because the acceptance probabilitywill be greater than one and the random number generated in step 1011must be equal to or less than one). However, in order to avoid becomingstuck on a local maxima of a fit score, the mechanism of FIG. 10 willalso sometimes accept an alteration that results in a worse fit score(as long as the calculated acceptance probability (step 1009) is greaterthan the random number generated in step 1011). As a result, thealignment optimization technique can move beyond a “local” optimizedsolution and locate an even better alignment. However, because the valueT is decreased with each iteration of the optimization loop, theacceptance probability (step 1009) calculated for a move to anorientation/alignment that results in a worse fit score will becomeincreasingly smaller each time the loop routine executes. As a result,although the optimization routine may be more willing to accept a moveto a less optimal orientation/scale early in the optimization process,it will be increasingly less likely to accept a worse orientation/scalesolution as the optimization routine progresses. By the time Tapproaches zero, the optimization routine is unlikely to change theorientation/scale of the surface model unless such a move would resultin an improved “fit score.”

Thus, the invention provides, among other things, a system for capturingCT data, generating a CT model, and removing artifacts from thegenerated CT model by capturing surface scan data of the patient'steeth, overlaying the surface scan data onto the CT model, andidentifying, reducing, and removing artifacts that are outside of thesurface scan data. Various features and advantages are set forth in thefollowing claims.

What is claimed is:
 1. A system for removing artifacts caused by x-ray reflective materials from an x-ray image, the system comprising: an x-ray source; an x-ray detector that captures x-ray images including a structure; an imaging device that captures a surface scan of the structure; and an image processor that constructs a three-dimensional CT model including the structure from the x-ray images and a three-dimensional surface model including the structure from the surface scan, the image processor resizing and orienting at least one of the surface model and the CT model so that the surface model and the CT model are of a same scale and orientation, overlaying the surface model on the CT model, detecting data points in the combined data set that extend beyond a surface of the structure in the surface model, wherein the detected data points represent artifacts in the CT model, adjusting the detected data points from the CT model by interpolation of the detected data points to create an artifact-reduced CT model, and displaying the artifact-reduced CT model.
 2. The system of claim 1, wherein the imaging device includes a laser scanning system.
 3. The system of claim 1, wherein the imaging device includes a structured light scanning system.
 4. The system of claim 1, wherein the image processor is configured to determine that all data points in the combined data set that extend beyond the surface of the structure in the surface model are artifacts, and remove the artifacts from the CT model.
 5. The system of claim 1, wherein the image processor is configured to compare a value of data points in the combined data set that extend beyond the surface of the structure in the surface model to a threshold, determine whether the detected data points are artifacts based on the comparison, and remove the artifacts from the CT model.
 6. The system of claim 1, wherein the image processor is configured to filter the data points in the combined data set that extend beyond the surface of the structure in the surface model, determine whether the detected data points are artifacts based on the filtering operation, and remove the artifacts from the CT model.
 7. The system of claim 1, wherein the image processor is configured to generate a first two-dimensional data slice from the surface model corresponding to a first two-dimensional data slice from the CT model, and detect data points in the combined data set that extend beyond a surface of the structure in the surface model by comparing the first two-dimensional data slice from the surface model to the first two-dimensional data slice from the CT model. 